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Educational Seminar:
Self-service BI, Logical Data Warehouse and Data Lakes
December 2016
Speakers
Chuck DeVries
VP, Enterprise Architecture
Vizient
Ravi Shankar
CMO
Denodo
Chris Walters
Sr. Solutions Consultant
...
Agenda1.Customer Use Case: Powering Self-Service BI with Logical
Data Warehouse and Operationalizing Logical Data Lakes
2....
Powering Self Service BI with Logical Data
Warehouses and Operationalizing Data
Lakes
Chuck DeVries
December 2016
AGENDA
- Who is Vizient
- Self Service BI on distributed data sets
- Modern Data Architecture
Vizient Presentation │ Date │ Confidential Information6
Who is Vizient?
• Combination of VHA, University HealthSystem
Cons...
Vizient Presentation │ Date │ Confidential Information7
Purpose, mission, strategic aspirations
Purpose
To ensure our memb...
Vizient Presentation │ Date │ Confidential Information8
Vizient members span the care continuum
Vizient serves thousands o...
Vizient Presentation │ Date │ Confidential Information9
Member-owned, member-driven
MEMBERSHIP BENEFITS
• Harness powerful...
We deliver brilliant, data-driven resources and
insights — from benchmarking and predictive analytics
to cost-savings — to...
Vizient Presentation │ Date │ Confidential Information11
Unmatched insight and expertise
9 out of 10
of the U.S. News & Wo...
Vizient Presentation │ Date │ Confidential Information12
Examples of powering self
service discovery with a
Logical Data Warehouse
approach
Vizient Presentation │ Date │ Confident...
Financial Data Mart
Primary Use Case: Unify disparate accounting and finance data marts
across various legacy organization...
Financial Data Mart
Architectural Approach
• Denodo was selected as the data platform in
order to utilize the following fe...
GPO Dashboard
Primary Use Case: Provide a consolidated view of supplier sales data
across all customers of legacy Vizient ...
GPO Dashboard
Key Challenges
• Balance between data timeliness and report performance
– Tableau reports performed best uti...
Contract Sales Actualizer Dashboard
Primary Use Case: Integrate Member Spend and Supplier Sales
data from all Vizient orga...
Contract Sales Actualizer Dashboard
Key Challenges
• Successful integration of Exadata RDM as a data source for Denodo
– A...
Modern Data Architecture
Vizient Presentation │ Date │ Confidential Information20
Virtual
warehouse
Modern Data Architecture
Vizient Presentation │ Date │ Confidential Information21
Open
Data
Purchase
Dat...
Our central focus is helping members
apply data and insights in new ways to
achieve sustainable results. Our
success is ul...
Logical Data Lakes/ Warehouse:
Architectural Patterns and Performance Considerations
Ravi Shankar, CMO
December 2016
Agenda1.The Logical Data Warehouse
2.Architectural Patterns
3.Performance Considerations
4.Customer Success Studies
Logical Data Warehouse
Description:
 A semantic layer on top of the data warehouse that keeps the business data
definitio...
Logical Data Warehouse
Description:
 “The Logical Data Warehouse (LDW) is a new data management architecture for
analytic...
29
Data Virtualization as the Data Integration Layer
Data Virtualization as Data
Integration/Semantic Layer
Data Virtualiz...
Logical Data Warehouse
30
EDW Hadoop
Cluster
Sales
HDFS
Files
Document
Collections
NoSQL
Database
ERP
Database Excel
What about the Logical Data Lake?
A Data Lake will not have a star or snowflake schema, but rather a more
heterogeneous co...
Architectural Patterns
For a Logical Data Warehouse
33
Common Patterns for a Logical Data Warehouse
1. The Virtual Data Mart
2. DW + MDM
3. DW + Cloud
4. DW + DW
5. DW histor...
34
1. Virtual Data Marts
Business friendly models defined on top of one or multiple systems,
often “flavored” for a partic...
35
1. Virtual Data Marts
Time Dimension Fact table
(sales)
Product
Retailer
Dimension
Sales
EDW Others
Product
Prod. Detai...
36
2. DW + MDM
Slim dimensions with extended information maintained in an external
MDM system
Motivation
 Keep a single c...
37
2. DW + MDM dimensions
Time Dimension Fact table
(sales) Product Dimension
Retailer
Dimension
EDW MDM
38
3. DW + Cloud dimensional data
Fresh data from cloud systems (e.g. SFDC) is mixed with the EDW, usually
on the dimensio...
39
3. DW + Cloud dimensional data
Time Dimension Fact table
(sales) Product Dimension
Customer
Dimension
CRM
SFDC
Customer...
40
4. Multiple DW integration
Motivation
 Merges and acquisitions
 Different DWs by department
 Transition to new EDW D...
41
4. Multiple DW integration
Time
Dimensi
on
Sales fact
Product
Dimension
Region
Finance EDW
City
Marketing EDW
Customer ...
42
5. DW Historical Partitioning
Only the most current data (e.g. last year) is in the EDW. Historical data is
offloaded t...
43
5. DW Historical offloading
Horizontal partitioning
Time Dimension Fact table
(sales) Product Dimension
Retailer
Dimens...
Performance Considerations
In a Logical Data Warehouse
45
It is a common assumption that a virtualized solution will
be much slower than a persisted approach via ETL:
1. There i...
46
Denodo has done extensive testing using queries from the standard benchmarking test
TPC-DS* and the following scenario
...
47
Performance Comparison
Query Description
Returned
Rows
Time Netezza
Time Denodo
(Federated Oracle,
Netezza & SQL Server...
48
Performance and optimizations in Denodo
Focused on 3 core concepts
Dynamic Multi-Source Query Execution Plans
Leverages...
49
Performance and optimizations in Denodo
Comparing optimizations in DV vs ETL
Although Data Virtualization is a data int...
Success Stories
Customer Case Studies
Autodesk Overview
• Founded 1982 (NASDAQ: ASDK)
• Annual revenues (FY 2015) $2.5B
 Over 8,800 employees
• 3D modeling and...
Business Drivers for Change
• Software consumption model is changing
 Perpetual licenses to subscriptions
 User want mor...
Technology Challenges
• Current ‘traditional’ BI/EDW architecture not
designed for data streams from online apps
 Weblogs...
Logical Data Warehouse at Autodesk
54
Logical Data Warehouse at Autodesk
Traditional BI/Reporting
55
Logical Data Warehouse at Autodesk
‘New Data’ Ingestion
56
Logical Data Warehouse at Autodesk
Reporting on Combined Data
57
58
Problem Solution Results
Case Study Autodesk Successfully Changes Their
Revenue Model and Transforms Business
 Autodes...
Demo
BIG DATA VIRTUALIZATION
DEPLOYMENT AND
MANAGEMENT
Best Practices
IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 61
IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 62
“Good work building ETL jobs this
year”
- No CEO Ever…
IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 63
SO WHY DO WE STILL BUILD THEM?
IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 64
BUSINESS VALUE IS KING
IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 65
BUSINESS VALUE IS KING
IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 66
BIGGER SURE ISN’T EASIER
• SKILLS
• EASY IN/HARD OUT
• ALL DATA SOURCES AREN...
IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 67
VIRTUALIZATION BRIDGES THE SKILLS
GAP
IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 68
VIRTUALIZATION PROVIDES EASE OF USE
How the data goes in… How it gets back o...
IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 69
SOMEBODY BOUGHT SOMETHING BACK
IN THE DAY
• WE HAVE TO DEAL WITH
LEGACY
• HO...
IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 70
WHAT NOW?
• POC USING DENODO
EXPRESS OR AWS
• IOLAP CAN HELP BUILD A
ROADMAP
Founded in 2000
 16 years Delivering Success
Headquartered in Frisco, Texas
 National Customer Base
 Extended Workforce...
Speakers
Chuck DeVries
VP, Enterprise Architecture
Vizient
Ravi Shankar
CMO
Denodo
Chris Walters
Sr. Solutions Consultant
...
Next Steps
Attend the webinar “Realizing the Promise
of Data Lakes” on December 15
Register at: www.denodo.com
Access Deno...
Thanks!
www.denodo.com info@denodo.com
© Copyright Denodo Technologies. All rights reserved
Unless otherwise specified, no...
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Education Seminar: Self-service BI, Logical Data Warehouse and Data Lakes

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This educational seminar took place on Thursday, December 8th in Westin Galleria Dallas, Texas.

Self-service BI, Logical Data Warehouse and Data Lakes – They are all essential components of Fast Data Strategy. Many companies are rapidly augmenting their traditional data warehouses, data marts, and ETL with their logical counterparts. Reason? Agility and rapid time-to-market.

Speakers including:
• Chuck DeVries, VP, Strategic Technology and Enterprise Architecture, Vizient,
• Ravi Shankar, Chief Marketing Officer, Denodo
• Charles Yorek, Vice President, iOLAP

Published in: Data & Analytics
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Education Seminar: Self-service BI, Logical Data Warehouse and Data Lakes

  1. 1. Educational Seminar: Self-service BI, Logical Data Warehouse and Data Lakes December 2016
  2. 2. Speakers Chuck DeVries VP, Enterprise Architecture Vizient Ravi Shankar CMO Denodo Chris Walters Sr. Solutions Consultant Denodo Charles Yorek VP, Business Analytics iOLAP
  3. 3. Agenda1.Customer Use Case: Powering Self-Service BI with Logical Data Warehouse and Operationalizing Logical Data Lakes 2.Logical Data Lakes/ Warehouse: Architectural Patterns and Performance Considerations 3.Demo: Building Logical Data Lakes/ Warehouse using Data Virtualization 4.Best Practices: Big Data Virtualization Deployment and Management 5.Panel: Self-Service BI, Logical Data Warehouse, Data Lakes
  4. 4. Powering Self Service BI with Logical Data Warehouses and Operationalizing Data Lakes Chuck DeVries December 2016
  5. 5. AGENDA - Who is Vizient - Self Service BI on distributed data sets - Modern Data Architecture
  6. 6. Vizient Presentation │ Date │ Confidential Information6 Who is Vizient? • Combination of VHA, University HealthSystem Consortium, Novation, MedAssets Spend and Clinical Resource Management and Sg2 • Experts with the purchasing power, insights and connections that accelerate performance for members
  7. 7. Vizient Presentation │ Date │ Confidential Information7 Purpose, mission, strategic aspirations Purpose To ensure our members deliver exceptional, cost- effective care Mission To connect members with the knowledge, solutions and expertise that accelerate performance Strategic Aspirations • Become an indispensable partner to health care organizations • Become a leader in health care innovation • Accelerate our growth rate
  8. 8. Vizient Presentation │ Date │ Confidential Information8 Vizient members span the care continuum Vizient serves thousands of health care organizations across the nation, from independent, community-based organizations to large, integrated systems including • Acute care hospitals • Academic medical centers • Non-acute community health care providers • Pediatric facilities
  9. 9. Vizient Presentation │ Date │ Confidential Information9 Member-owned, member-driven MEMBERSHIP BENEFITS • Harness powerful insights • Accelerate performance • Achieve scale and efficiency • Make innovative connections • Be more agile • Build knowledge • Gain advocates on important policy issues We measure our success by our members’ success. We fuel powerful connections that help members focus on what they do best: deliver exceptional, cost-effective care.
  10. 10. We deliver brilliant, data-driven resources and insights — from benchmarking and predictive analytics to cost-savings — to where they’re needed most. Empowering brilliant connections
  11. 11. Vizient Presentation │ Date │ Confidential Information11 Unmatched insight and expertise 9 out of 10 of the U.S. News & World Report Best Hospitals 2014-2015 Honor Roll utilized our contracts and services. ~$100B Vizient represents approximately $100 billion in annual purchasing volume — the largest in the industry. 200+ Vizient member hospitals have achieved remarkable improvements in quality and patient safety through our Hospital Engagement Network. More than 1/3 Vizient provides services to more than one-third of the nation’s hospitals. Information is inclusive of MedAssets Spend and Clinical Resource Management segment, including Sg2.
  12. 12. Vizient Presentation │ Date │ Confidential Information12
  13. 13. Examples of powering self service discovery with a Logical Data Warehouse approach Vizient Presentation │ Date │ Confidential Information13
  14. 14. Financial Data Mart Primary Use Case: Unify disparate accounting and finance data marts across various legacy organizations into a logical data warehouse Secondary Use Cases • Provide a unified source for key BI initiatives like the GPO Dashboard • Support reporting needs as legacy systems are migrated or replaced during integration of Vizient and L-MDAS (dbVision, etc.) • Provide a final resting place for archived legacy sources like Solomon, Epicor, etc. Vizient Presentation │ Date │ Confidential Information14 VHA MedAssets UHC
  15. 15. Financial Data Mart Architectural Approach • Denodo was selected as the data platform in order to utilize the following features of the software: –Data Virtualization allows sources in various mediums and locations to be integrated without physically moving the data –Data Abstraction allows data to be represented consistently within the datamart while data sources are moved or replaced behind the scenes –Data Integration allows for a single seamless view to be created across a subject area (e.g. “Supplier Sales”) with varied data transformation rules for each data source within the subject area (PRS, dbVision) allowing a logical data warehouse to be created without the need to instantiate a physical on Vizient Presentation │ Date │ Confidential Information15
  16. 16. GPO Dashboard Primary Use Case: Provide a consolidated view of supplier sales data across all customers of legacy Vizient & Med Assets organizations. Architectural Approach • Financial Datamart (on Denodo) for data source • Denodo TDE Exporter Tool for daily data extracts to Tableau: – Report Data – Report User Security • Tableau for report development and distribution Vizient Presentation │ Date │ Confidential Information16 Over 400 active users across 6 departments
  17. 17. GPO Dashboard Key Challenges • Balance between data timeliness and report performance – Tableau reports performed best utilizing the TDE format (cached/extracted dataset) as opposed to a live connection – This meant that the report caches required daily refreshes, and data extraction had to be appropriately tuned – Denodo features such as dataset statistics and indexing greatly contributed to this performance tuning • Provisioning user security at cell level – The requirement for some internal report users to be restricted to the members/customers to which they are assigned meant that a new report security approach was needed – Reliance on TDEs for report data necessitated the integration of security in the reporting layer – Tableau’s “data blending” feature allows user security to be specified within a separate dataset – This also supports reuse of the security view across logical data warehouse views Vizient Presentation │ Date │ Confidential Information17
  18. 18. Contract Sales Actualizer Dashboard Primary Use Case: Integrate Member Spend and Supplier Sales data from all Vizient organizations to identify opportunities for increasing contract utilization Other Use Cases: • Maintain consistency (Single Source Of Truth) with GPO dashboard regarding: – Supplier Sales Data – Dimension Data – User Security Architectural Approach • Data source utilizes Denodo to reuse overlapping datasets (sales, dimensions, security) while allowing separate virtualized views to be created for new datasets (member spend) which can be also be reused by future projects via a logical data warehouse • Reporting components match approach used by GPO Dashboard Vizient Presentation │ Date │ Confidential Information18
  19. 19. Contract Sales Actualizer Dashboard Key Challenges • Successful integration of Exadata RDM as a data source for Denodo – Approach utilizes the strength of Exadata RDBMS for aggregating large quantities of data quickly – Denodo to integrate the data with similar legacy SQL Server data sources to create a comprehensive view of Vizient member spend • Scalability/Configuration Management – Advances were made to support parallel development of this project and continued efforts on GPO dashboard – Compartmentalization features within Denodo allow for code changes in each project to be version controlled and assessed for dependencies – Process guidelines are being authored to allow for multiple development efforts on the same datasets Vizient Presentation │ Date │ Confidential Information19
  20. 20. Modern Data Architecture Vizient Presentation │ Date │ Confidential Information20
  21. 21. Virtual warehouse Modern Data Architecture Vizient Presentation │ Date │ Confidential Information21 Open Data Purchase Data RDBMS Rules RDBMS ODS Data warehouse
  22. 22. Our central focus is helping members apply data and insights in new ways to achieve sustainable results. Our success is ultimately defined by the success of our members in serving their patients and communities. Curt Nonomaque, President and CEO, Vizient
  23. 23. Logical Data Lakes/ Warehouse: Architectural Patterns and Performance Considerations Ravi Shankar, CMO December 2016
  24. 24. Agenda1.The Logical Data Warehouse 2.Architectural Patterns 3.Performance Considerations 4.Customer Success Studies
  25. 25. Logical Data Warehouse Description:  A semantic layer on top of the data warehouse that keeps the business data definition.  Allows the integration of multiple data sources including enterprise systems, the data warehouse, additional processing nodes (analytical appliances, Big Data, …), Web, Cloud and unstructured data.  Publishes data to multiple applications and reporting tools. 27
  26. 26. Logical Data Warehouse Description:  “The Logical Data Warehouse (LDW) is a new data management architecture for analytics combining the strengths of traditional repository warehouses with alternative data management and access strategy. The LDW will form a new best practice by the end of 2015.”  “The LDW is an evolution and augmentation of DW practices, not a replacement”  “A repository-only style DW contains a single ontology/taxonomy, whereas in the LDW a semantic layer can contain many combination of use cases, many business definitions of the same information”  “The LDW permits an IT organization to make a large number of datasets available for analysis via query tools and applications.” 28 Gartner Definition Gartner Hype Cycle for Enterprise Information Management, 2012
  27. 27. 29 Data Virtualization as the Data Integration Layer Data Virtualization as Data Integration/Semantic Layer Data Virtualization EDW ODS • Move data integration and semantic layer to independent Data Virtualization platform • Purpose built for supporting data access across multiple heterogeneous data sources • Separate layer provides semantic models for underlying data • Physical to logical mapping • Enforces common and consistent security and governance policies • Gartner’s recommended approach
  28. 28. Logical Data Warehouse 30 EDW Hadoop Cluster Sales HDFS Files Document Collections NoSQL Database ERP Database Excel
  29. 29. What about the Logical Data Lake? A Data Lake will not have a star or snowflake schema, but rather a more heterogeneous collection of views with raw data from heterogeneous sources The virtual layer will act as a common umbrella under which these different sources are presented to the end user as a single system However, from the virtualization perspective, a Virtual Data Lake shares many technical aspects with a LDW and most of these contents also apply to a Logical Data Lake
  30. 30. Architectural Patterns For a Logical Data Warehouse
  31. 31. 33 Common Patterns for a Logical Data Warehouse 1. The Virtual Data Mart 2. DW + MDM 3. DW + Cloud 4. DW + DW 5. DW historical offloading
  32. 32. 34 1. Virtual Data Marts Business friendly models defined on top of one or multiple systems, often “flavored” for a particular division Motivation  Hide complexity of star schemas for business users  Simplify model for a particular vertical  Reuse semantic models and security across multiple reporting engines Typical queries  Simple projections, filters and aggregations on top of curated “fat tables” that merge data from facts and many dimensions Simplified semantic models for business users
  33. 33. 35 1. Virtual Data Marts Time Dimension Fact table (sales) Product Retailer Dimension Sales EDW Others Product Prod. Details
  34. 34. 36 2. DW + MDM Slim dimensions with extended information maintained in an external MDM system Motivation  Keep a single copy of golden records in the MDM that can be reused across systems and managed in a single place Typical queries  Join a large fact table (DW) with several MDM dimensions, aggregations on top Example  Revenue by customer, projecting the address from the MDM
  35. 35. 37 2. DW + MDM dimensions Time Dimension Fact table (sales) Product Dimension Retailer Dimension EDW MDM
  36. 36. 38 3. DW + Cloud dimensional data Fresh data from cloud systems (e.g. SFDC) is mixed with the EDW, usually on the dimensions. DW is sometimes also in the cloud. Motivation  Take advantage of “fresh” data coming straight from SaaS systems  Avoid local replication of cloud systems Typical queries  Dimensions are joined with cloud data to filter based on some external attribute not available (or not current) in the EDW Example  Report on current revenue on accounts where the potential for an expansion is higher than 80%
  37. 37. 39 3. DW + Cloud dimensional data Time Dimension Fact table (sales) Product Dimension Customer Dimension CRM SFDC Customer EDW
  38. 38. 40 4. Multiple DW integration Motivation  Merges and acquisitions  Different DWs by department  Transition to new EDW Deployments (migration to Spark, Redshift, etc.) Typical queries  Joins across fact tables in different DW with aggregations before or after the JOIN Example  Get customers with a purchases higher than 100 USD that do not have a fidelity card (purchases and fidelity card data in different DW) Use of multiple DW as if it was only one
  39. 39. 41 4. Multiple DW integration Time Dimensi on Sales fact Product Dimension Region Finance EDW City Marketing EDW Customer Fidelity factsProduct Dimension *Real Examples: Nationwide POC, IBM tests Store
  40. 40. 42 5. DW Historical Partitioning Only the most current data (e.g. last year) is in the EDW. Historical data is offloaded to a Hadoop cluster Motivations  Reduce storage cost  Transparently use the two datasets as if they were all together Typical queries  Facts are defined as a partitioned UNION based on date  Queries join the “virtual fact” with dimensions and aggregate on top Example  Queries on current date only need to go to the DW, but longer timespans need to merge with Hadoop Horizontal partitioning
  41. 41. 43 5. DW Historical offloading Horizontal partitioning Time Dimension Fact table (sales) Product Dimension Retailer Dimension Current Sales Historical Sales EDW
  42. 42. Performance Considerations In a Logical Data Warehouse
  43. 43. 45 It is a common assumption that a virtualized solution will be much slower than a persisted approach via ETL: 1. There is a large amount of data moved through the network for each query 2. Network transfer is slow But is this really true?
  44. 44. 46 Denodo has done extensive testing using queries from the standard benchmarking test TPC-DS* and the following scenario Compares the performance of a federated approach in Denodo with an MPP system where all the data has been replicated via ETL Customer Dim. 2 M rows Sales Facts 290 M rows Items Dim. 400 K rows * TPC-DS is the de-facto industry standard benchmark for measuring the performance of decision support solutions including, but not limited to, Big Data systems. vs. Sales Facts 290 M rows Items Dim. 400 K rows Customer Dim. 2 M rows Performance Comparison Logical Data Warehouse vs. Physical Data Warehouse
  45. 45. 47 Performance Comparison Query Description Returned Rows Time Netezza Time Denodo (Federated Oracle, Netezza & SQL Server) Optimization Technique (automatically selected) Total sales by customer 1,99 M 20.9 sec. 21.4 sec. Full aggregation push-down Total sales by customer and year between 2000 and 2004 5,51 M 52.3 sec. 59.0 sec Full aggregation push-down Total sales by item brand 31,35 K 4.7 sec. 5.0 sec. Partial aggregation push-down Total sales by item where sale price less than current list price 17,05 K 3.5 sec. 5.2 sec On the fly data movement Logical Data Warehouse vs. Physical Data Warehouse
  46. 46. 48 Performance and optimizations in Denodo Focused on 3 core concepts Dynamic Multi-Source Query Execution Plans Leverages processing power & architecture of data sources Dynamic to support ad hoc queries Uses statistics for cost-based query plans Selective Materialization Intelligent Caching of only the most relevant and often used information Optimized Resource Management Smart allocation of resources to handle high concurrency Throttling to control and mitigate source impact Resource plans based on rules
  47. 47. 49 Performance and optimizations in Denodo Comparing optimizations in DV vs ETL Although Data Virtualization is a data integration platform, architecturally speaking it is more similar to a RDBMs Uses relational logic Metadata is equivalent to that of a database Enables ad hoc querying Key difference between ETL engines and DV: ETL engines are optimized for static bulk movements Fixed data flows Data virtualization is optimized for queries Dynamic execution plan per query Therefore, the performance architecture presented here resembles that of a RDBMS
  48. 48. Success Stories Customer Case Studies
  49. 49. Autodesk Overview • Founded 1982 (NASDAQ: ASDK) • Annual revenues (FY 2015) $2.5B  Over 8,800 employees • 3D modeling and animation software  Flagship product is AutoCAD • Market sectors:  Architecture, Engineering, and Construction  Manufacturing  Media and Entertainment  Recently started 3D Printing offerings 51
  50. 50. Business Drivers for Change • Software consumption model is changing  Perpetual licenses to subscriptions  User want more flexibility in how they use software • Autodesk needed to transition to subscription pricing  2016 – some products will be subscription only • Lifetime revenue higher with subscriptions  Over 3-5 years, subscriptions = more revenues • Changing a licensing model is disruptive 52
  51. 51. Technology Challenges • Current ‘traditional’ BI/EDW architecture not designed for data streams from online apps  Weblogs, Clickstreams, Cloud/Desktop apps, etc. • Existing infrastructure can’t simply ‘go away’  Regulatory reporting (e.g. SEC)  Existing ‘perpetual’ customers • ‘Subscription’ infrastructure work in parallel  Extend and enhance existing systems  With single access point to all data • Solution – ‘Logical Data Warehouse’ 53
  52. 52. Logical Data Warehouse at Autodesk 54
  53. 53. Logical Data Warehouse at Autodesk Traditional BI/Reporting 55
  54. 54. Logical Data Warehouse at Autodesk ‘New Data’ Ingestion 56
  55. 55. Logical Data Warehouse at Autodesk Reporting on Combined Data 57
  56. 56. 58 Problem Solution Results Case Study Autodesk Successfully Changes Their Revenue Model and Transforms Business  Autodesk was changing their business revenue model from a conventional perpetual license model to subscription-based license model.  Inability to deliver high quality data in a timely manner to business stakeholders.  Evolution from traditional operational data warehouse to contemporary logical data warehouse deemed necessary for faster speed.  General purpose platform to deliver data through logical data warehouse.  Denodo Abstraction Layer helps live invoicing with SAP.  Data virtualization enabled a culture of “see before you build”.  Successfully transitioned to subscription-based licensing.  For the first time, Autodesk can do single point security enforcement and have uniform data environment for access. Autodesk, Inc. is an American multinational software corporation that makes software for the architecture, engineering, construction, manufacturing, media, and entertainment industries.
  57. 57. Demo
  58. 58. BIG DATA VIRTUALIZATION DEPLOYMENT AND MANAGEMENT Best Practices
  59. 59. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 61
  60. 60. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 62 “Good work building ETL jobs this year” - No CEO Ever…
  61. 61. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 63 SO WHY DO WE STILL BUILD THEM?
  62. 62. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 64 BUSINESS VALUE IS KING
  63. 63. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 65 BUSINESS VALUE IS KING
  64. 64. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 66 BIGGER SURE ISN’T EASIER • SKILLS • EASY IN/HARD OUT • ALL DATA SOURCES AREN’T EQUAL
  65. 65. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 67 VIRTUALIZATION BRIDGES THE SKILLS GAP
  66. 66. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 68 VIRTUALIZATION PROVIDES EASE OF USE How the data goes in… How it gets back out…
  67. 67. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 69 SOMEBODY BOUGHT SOMETHING BACK IN THE DAY • WE HAVE TO DEAL WITH LEGACY • HOMOGENEITY ISN’T REALISTIC • ALL DATA SOURCES AREN’T EQUAL
  68. 68. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 70 WHAT NOW? • POC USING DENODO EXPRESS OR AWS • IOLAP CAN HELP BUILD A ROADMAP
  69. 69. Founded in 2000  16 years Delivering Success Headquartered in Frisco, Texas  National Customer Base  Extended Workforce U.S. Company with Offshore Capabilities  60 consultants in the U.S. (full-time, salaried)  50 consultants in Europe (Offshore – BIDC) IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL IOLAP OVERVIEW Focused solely on big data, data strategy, advanced analytics, and reporting 71 Onsite Near Shore Offshore
  70. 70. Speakers Chuck DeVries VP, Enterprise Architecture Vizient Ravi Shankar CMO Denodo Chris Walters Sr. Solutions Consultant Denodo Charles Yorek VP, Business Analytics iOLAP
  71. 71. Next Steps Attend the webinar “Realizing the Promise of Data Lakes” on December 15 Register at: www.denodo.com Access Denodo on AWS Visit: www.denodo.com/en/denodo-platform/denodo-platform-for-aws Download Denodo Express The free way to Data Virtualization! Download from: www.denodo.com
  72. 72. Thanks! www.denodo.com info@denodo.com © Copyright Denodo Technologies. All rights reserved Unless otherwise specified, no part of this PDF file may be reproduced or utilized in any for or by any means, electronic or mechanical, including photocopying and microfilm, without prior the written authorization from Denodo Technologies.

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